Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art

Amreen M. Dinani, Kris V. Kowdley, Mazen Noureddin

Research output: Contribution to journalReview articlepeer-review

25 Scopus citations

Abstract

The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual’s journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.

Original languageEnglish
Pages (from-to)2233-2240
Number of pages8
JournalHepatology
Volume74
Issue number4
DOIs
StatePublished - Oct 2021

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